Defining the role of olfactory ensheathing cells in facilitating axon remyelination following damage to the spinal cord
Bibliographic record
Abstract
Olfactory ensheathing cells (OECs) are unique cells that are responsible for the successful regeneration of olfactory axons throughout the life of adult mammals. More than a decade of research has shown that implantation of OECs may be a promising therapy for damage to the nervous system, including spinal cord injury. Based on this research, several clinical trials worldwide have been initiated that use autologous transplantation of olfactory tissue containing OECs into the damaged spinal cord of humans. However, research from several laboratories has challenged the widely held belief that OECs are directly responsible for myelinating axons and promoting axon regeneration. The purpose of this review is to provide a working hypothesis that integrates several current ideas regarding the mechanisms of the beneficial effects of OECs. Specifically, OECs promote axon regeneration and functional recovery indirectly by augmenting the endogenous capacity of host Schwann cells to invade the damaged spinal cord. Together with Schwann cells, OECs create a 3-dimensional matrix that provides a permissive microenvironment for successful axon regeneration in the adult mammalian central nervous system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".